Sr. Director, Applied Researcher

Capital One • McLean, VA

1d • $350,000 - $435,700

About The Position

The Sr. Director, Applied Researcher will lead teams in Capital One’s AI Foundations organization and set strategic research direction in collaboration with Applied Science, Engineering, Product, technology, and business leaders. The role involves guiding and mentoring applied scientists, representing Capital One in the research community, and collaborating with prominent faculty members in relevant AI research fields.

The position partners with applied researchers, data scientists, AI engineers, machine learning engineers, and product managers to develop AI-powered products. It applies AI foundation models throughout the development lifecycle—from design and training to evaluation, validation, and implementation—and advances applied research into customer experiences. The role also translates technical work into business goals and aligns long-term scientific research with business transformation priorities.

The successful candidate brings deep expertise in AI methodologies, large-scale deep learning, and production systems, and has a record of research contributions and delivering models at scale. The role may focus on areas such as large language models, behavioral models, training and inference optimization, fine-tuning, or data preparation.

Requirements

• PhD in Electrical Engineering, Computer Engineering, Computer Science, AI, Mathematics, or a related field, plus 6 years of experience in Applied Research; or an M.S. in one of those fields, plus 8 years of experience in Applied Research.
• At least 5 years of people leadership experience.
• Hands-on experience developing AI foundation models and solutions using open-source tools and cloud computing platforms.
• Deep, first-principles understanding of AI methodologies.
• Experience building large deep learning models for language, images, events, or graphs, and expertise in at least one area such as training optimization, self-supervised learning, robustness, explainability, or reinforcement learning.
• Experience building production-grade systems with large-scale business impact.
• Engineering mindset and a track record of delivering models at scale in training data and inference volumes.
• Experience delivering libraries, platform-level code, or solution-level code to existing products.
• A record of high-quality machine-learning ideas or improvements, demonstrated through accomplishments such as first-author publications or projects.
• Ability to own and pursue a research agenda, choose impactful problems, and autonomously carry out long-running projects.

Nice To Haves

• PhD in Computer Science, Machine Learning, Computer Engineering, Applied Mathematics, Electrical Engineering, or a related field.
• PhD focus on NLP, or a master’s degree plus 10 years of industrial NLP research experience.
• Core contributor to a team that trained a large language model from scratch with 10B+ parameters and 500B+ tokens.
• Numerous publications at ACL, NAACL, EMNLP, NeurIPS, ICML, or ICLR on large-language-model pre-training topics.
• Experience working on an open-source or commercial LLM currently available for use.
• Ability to guide the technical direction of a large-scale model-training team.
• Experience working with GPU clusters of 500 or more nodes.
• Experience with an LLM scaled to 70B parameters and 1T+ tokens.
• Experience with training optimization frameworks such as DeepSpeed or NeMo.
• PhD focus on geometric deep learning topics such as graph neural networks, sequential models, or multivariate time series.
• Technical leadership experience in deploying a very large user behavior model.
• Multiple publications on training models on graph and sequential data structures at KDD, ICML, NeurIPS, or ICLR.
• Experience scaling graph models to more than 50 million nodes.
• Experience with large-scale deep-learning-based recommender systems.
• Experience with production real-time and streaming environments.
• Contributions to open-source frameworks such as PyTorch Geometric or DGL.
• Proposed new inference or representation-learning methods for graphs or sequences.
• Experience working with datasets containing 100 million or more users.
• PhD focus on optimizing training of very large language models.
• At least 5 years of experience and/or publications in model sparsification, quantization, training parallelism or partitioning design, gradient checkpointing, or model compression.
• PhD focus on guiding LLMs with further tasks, such as supervised fine-tuning, instruction tuning, dialogue fine-tuning, or parameter tuning.
• Knowledge of transfer learning, model adaptation, and model guidance principles.
• Experience deploying a fine-tuned large language model.
• Numerous publications studying tokenization, data quality, dataset curation, or labeling.
• Leading contributions to one or more large open-source corpora of 1 trillion or more tokens.
• Core contributor to open-source libraries for data quality, dataset curation, or labeling.
• Track record of scaling research organizations and establishing cross-disciplinary programs that translate cutting-edge AI research into enterprise impact.

Responsibilities

• Partner with applied researchers, data scientists, AI engineers, machine learning engineers, and product managers to deliver AI-powered products.
• Use technologies such as PyTorch, AWS Ultraclusters, Hugging Face, and Lightning to analyze large volumes of numeric and textual data.
• Build and apply AI foundation models across design, training, evaluation, validation, and implementation.
• Conduct high-impact applied research and apply emerging AI developments to customer experiences.
• Translate technical work into tangible business goals.
• Set the organization’s strategic research vision and roadmap, aligning scientific discovery with business transformation priorities.
• Lead, guide, and mentor a team of applied scientists.
• Represent Capital One in the research community and collaborate with prominent faculty members.
• Own and pursue a research agenda, including selecting impactful problems and carrying out long-running projects.
• Guide technical direction and contribute to production-grade AI systems and solutions that deliver large-scale business impact.

Benefits

• Comprehensive health, financial, and other benefits supporting total well-being.
• Performance-based incentive compensation, which may include cash bonuses and/or long-term incentives.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior